Ruchi Mangharamani
Georgia State University
Atlanta, GA, 30302, United States
Er. Shubham Jain
IIT Bombay
Main Gate Rd, IIT Area, Powai, Mumbai, Maharashtra 400076, India
Abstract
Real-Time AI Risk Intelligence Dashboards are revolutionizing the way health insurers detect fraud and adjust policies. By harnessing advanced machine learning algorithms and real-time data analytics, these dashboards integrate vast and diverse datasets to identify irregularities and emerging fraud patterns swiftly. This innovative approach enhances traditional detection methods, enabling insurers to transition from reactive to proactive fraud management. The dashboards continuously monitor claims, patient records, provider activities, and external data sources, ensuring that suspicious trends are flagged as they occur. This real-time insight not only minimizes financial losses but also streamlines the claims review process, reduces administrative costs, and improves overall service delivery. Furthermore, the integration of risk intelligence into policy adjustment procedures empowers insurers to tailor policies dynamically in response to evolving risk profiles. This adaptability promotes a more resilient insurance framework that can respond to market changes and regulatory requirements effectively. The system’s predictive capabilities allow for early intervention strategies, mitigating potential fraudulent activities before they escalate. The paper highlights case studies demonstrating significant improvements in fraud detection rates and policy effectiveness. It also discusses the challenges of data integration, model transparency, and ethical considerations related to automated decision-making. Ultimately, the adoption of Real-Time AI Risk Intelligence Dashboards presents a paradigm shift in the health insurance industry, fostering a culture of continuous improvement and resilience against fraud while ensuring policies remain fair and responsive to the needs of both insurers and policyholders.
Keywords
Real-time analytics, AI risk intelligence dashboards, health insurance fraud detection, dynamic policy adjustments, predictive modeling, machine learning, risk management.
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